
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "103"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 101 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 97 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	0.00%	0.00%	0.00%	0.00%	1.08%	1.62%	0.873112	-0.364452	-0.311566	-1.039732	-0.468635	-0.506376	-0.486199	-1.161940	0.8053	0.8115	0.2355	3.146041	3.255488
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	1.08%	0.966078	0.055951	-0.624764	-0.882426	-0.523657	-1.036952	-0.824354	-0.971334	0.7520	0.7455	0.2430	2.594387	2.361937
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.551378	-1.018085	1.363065	0.823253	-0.284618	-1.017988	1.026798	-0.080325	0.0290	0.0300	0.0002	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	0.742376	0.105226	-0.770089	-0.851637	0.163006	-0.365833	-0.533947	-0.924920	0.6848	0.6979	0.3599	1.717356	1.676678
2459590	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	1.431190	0.412562	-0.671820	-0.774092	-0.456138	0.853557	-0.393678	-0.673915	0.6879	0.6965	0.3538	1.811481	1.689512
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.54%	0.497567	-0.333442	-0.627786	-0.635399	-0.252150	-1.194999	-0.919034	-0.960031	0.7307	0.7252	0.2733	1.948973	1.862672
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.54%	0.879344	-0.541075	-0.877186	-1.088719	0.069080	-1.206122	-0.390503	-1.114143	0.7922	0.7806	0.2088	5.446651	3.888451
2459587	digital_ok	0.00%	-	-	-	100.00%	0.00%	1.224759	0.187764	-0.318139	-0.673521	0.388088	0.986476	-0.655821	-1.103126	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	11.89%	5.95%	0.596550	0.765863	-0.824288	1.043261	-0.019709	1.271515	-0.379143	-1.398970	0.6743	0.6821	0.3539	1.517336	1.566937
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.930149	-0.419941	-0.684801	1.735400	1.875776	0.679270	1.156452	-0.621231	0.0301	0.0294	0.0009	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	18.668609	18.329863	5.585171	5.676902	3.393413	4.160678	-0.502861	-0.865409	0.6874	0.6985	0.3721	0.000000	0.000000
2459583	digital_ok	0.00%	0.00%	0.00%	0.00%	3.23%	96.77%	1.401946	0.810087	0.502588	0.683561	0.391652	0.957484	-0.732339	-0.982693	0.6863	0.6944	0.3753	12.131412	2.625975
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	45.95%	0.00%	1.059334	0.764368	0.694906	0.535909	0.591603	1.261497	0.550063	-0.867492	0.6877	0.6978	0.3750	1.401685	1.323716
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	19.46%	0.54%	1.481089	0.964285	-0.161550	0.616128	-0.037350	1.352037	-0.803699	-0.979963	0.6995	0.7059	0.3678	1.719354	1.709337
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.217030	0.737165	0.673246	0.768236	0.903290	1.396508	-1.108677	-1.037344	0.6886	0.6984	0.3726	0.000000	0.000000
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	1.541669	0.876542	0.934063	0.922612	0.301287	1.421298	-0.932127	-0.900732	0.6869	0.6978	0.3744	1.471357	1.431011
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.897509	-0.018506	1.009208	1.435148	0.433125	-0.016804	0.849775	-0.970166	0.0287	0.0283	0.0005	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	1.058008	0.407568	-0.037965	0.401256	-0.043167	0.862890	-0.918368	-0.983279	0.6915	0.7016	0.3704	1.623607	1.549440
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	0.00%	0.589057	0.074775	-0.207730	-0.173960	-0.193530	-0.024941	-0.830282	-0.840651	0.7111	0.7215	0.3723	1.494788	1.498298
2459575	digital_ok	0.00%	0.00%	0.00%	0.00%	4.84%	1.08%	0.369546	0.012887	-0.035597	0.000594	0.099913	0.556024	-0.300239	-0.325648	0.7696	0.7711	0.3166	1.626499	1.626078
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	0.839409	0.439203	-0.247336	-0.186245	-0.641115	-0.456635	-0.324689	-0.805483	0.7063	0.7161	0.3696	1.618098	1.646033
2459573	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	1.389074	1.033978	0.943221	1.278029	-0.250277	1.005643	-1.068492	-0.763529	0.7094	0.7179	0.3687	1.451172	1.492698
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	27.57%	0.00%	0.908087	0.348130	-0.041885	-0.019056	-0.527959	0.110857	-0.397022	-0.561603	0.7131	0.7207	0.3670	1.470422	1.459410
2459571	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.790013	-0.149324	1.056384	1.296080	-0.021906	-0.502934	0.644979	-0.961284	0.0400	0.0289	0.0026	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	0.397619	-0.026710	-0.255612	0.075604	-0.080239	-0.192105	-0.723247	-0.325690	0.7837	0.7867	0.2264	2.217942	1.996685
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	0.783732	0.515023	0.028293	0.556524	-0.850446	0.682540	-0.937797	-1.097584	0.7010	0.7090	0.3837	1.484920	1.491308
2459565	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	6.94%	0.00%	-0.447156	-0.514355	-1.566920	-0.246888	-0.989288	1.260957	-0.477051	-0.359456	0.7205	0.7209	0.3782	1.707190	1.546312
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	1.114825	0.562406	1.477811	-0.394631	0.896920	1.445011	-0.827635	-0.670272	0.7128	0.7172	0.3789	1.671922	1.595162
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.447625	-0.967800	-0.800196	-0.902269	-0.531035	-0.599487	-0.262594	-0.304951	0.7152	0.7231	0.3753	1.725609	1.611019
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.345047	-0.980114	-1.035263	-1.176841	-0.958392	-1.072122	-0.586257	-0.817294	0.7182	0.7265	0.3751	1.613840	1.599302
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.144064	-0.803376	-0.775639	-1.126780	-0.535647	-0.881337	-0.204888	-0.597029	0.7103	0.7183	0.3834	1.632036	1.530400
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.91%	0.54%	-0.663114	-0.976093	-0.849078	-1.114752	-1.081981	-1.204595	-0.316695	-0.518561	0.7124	0.7193	0.3706	1.614800	1.511551
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.594431	0.619177	0.821178	0.734861	0.066040	0.558988	-0.668346	-0.885987	0.7150	0.7192	0.3782	1.557119	1.501683
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0295	0.0300	0.0003	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.868987	0.710305	0.580399	0.543633	-0.503425	0.392556	-1.065241	-1.089706	0.7145	0.7201	0.3745	1.626492	1.552881
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7164	0.7222	0.3842	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7140	0.7165	0.3812	nan	nan
2459552	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.698055	0.489950	-0.042395	1.572759	-0.204364	0.435948	-0.718038	-1.075037	0.7238	0.7260	0.3727	0.622401	0.648047
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.362470	-0.906561	-0.638390	-1.245214	-1.168715	-1.337867	-0.293438	-0.558849	0.7182	0.7227	0.3698	1.616385	1.489077
2459550	digital_ok	-	96.71%	96.71%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0525	0.0537	0.0009	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.681912	0.940945	-0.321059	0.736238	0.768711	0.461440	-0.834091	-1.090085	0.7163	0.7240	0.3810	1.659874	1.626004
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	0.536040	1.155273	0.550847	-0.506895	-0.361561	1.103835	-0.969698	-1.039749	0.7635	0.7700	0.3350	1.803507	1.550078
2459541	digital_ok	0.00%	1.34%	1.34%	0.00%	21.88%	0.00%	0.408534	0.193646	0.139758	0.585441	-0.211138	1.277194	-1.115177	-0.576248	0.7575	0.7592	0.3154	1.443335	1.483999
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.00%	-0.132905	-0.428946	-1.175203	-1.210634	-0.395827	0.008163	-0.815602	-0.888754	0.7653	0.7649	0.3289	1.677030	1.504167
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	5.93%	0.00%	0.346264	0.202056	0.220075	1.125673	0.922557	0.742566	-0.980628	-1.442200	0.7444	0.7416	0.3994	1.545378	1.465567
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.277874	-0.200854	-1.069005	-1.144919	-0.617417	0.125299	-0.793293	-0.729953	0.7941	0.7854	0.3368	1.658058	1.494445
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.00%	-0.359570	-0.556998	-1.132635	-1.251640	0.107671	-0.006339	-0.549088	-0.731956	0.7834	0.7730	0.3381	1.641282	1.585463
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	0.640497	0.402031	1.073747	1.192526	1.475060	0.697828	-0.932837	-0.820565	0.7316	0.7406	0.3825	1.485407	1.441862
2459532	digital_ok	0.00%	0.00%	0.00%	0.00%	5.00%	0.00%	1.143318	1.059300	1.163827	1.195891	1.125046	0.892835	-0.421259	-0.842762	0.7353	0.7378	0.3763	1.536703	1.492192
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	4.73%	0.00%	-0.746722	-0.820927	-0.796746	-0.990473	-1.048995	-0.900448	-0.658334	-0.560976	0.7483	0.7485	0.3823	1.609984	1.526229
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.446302	0.659831	0.848991	1.390001	-0.531498	-1.037216	-0.898356	-0.828955	0.9169	0.9461	0.2786	0.000000	0.000000
2459523	digital_ok	0.00%	-	-	-	12.24%	0.00%	0.035304	0.427793	0.004163	1.425982	0.157791	-0.683185	-1.119033	-1.279783	nan	nan	nan	2.222492	2.043493
2459522	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.432552	-0.005951	0.574706	1.066938	-0.692531	-0.265270	-0.486502	-0.566255	0.7067	0.7359	0.3237	1.985106	1.801171
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.743993	-0.025685	0.670108	0.790363	0.189232	-0.999739	0.779847	-0.801570	0.0452	0.0380	0.0057	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	0.433911	0.233589	0.253203	0.639456	-1.025254	-0.864922	-0.677061	-0.230541	0.9267	0.9590	0.1570	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.587484	-0.149848	-0.039994	-0.002892	0.616906	-0.804961	-0.705792	-0.954466	0.8918	0.8754	0.1822	5.703400	4.043983
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	0.544831	0.730950	0.123805	1.101677	-0.432614	1.137773	-0.561708	-0.948542	0.8887	0.8859	0.4168	0.000000	0.000000
2459501	digital_ok	0.00%	0.00%	0.00%	0.00%	76.74%	0.00%	1.430249	1.462759	0.913262	1.625571	-0.711062	0.108974	-0.826212	-1.250142	0.8886	0.9172	0.3681	0.000000	0.000000
2459500	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	1.355699	1.818563	0.890218	1.524171	0.381655	1.626655	-0.891332	-1.060255	0.8973	0.9148	0.3595	0.000000	0.000000
2459499	digital_ok	0.00%	0.00%	0.00%	0.00%	97.37%	0.00%	1.536650	1.848548	1.613226	1.704338	1.177080	1.455550	-1.108891	-1.205844	0.8945	0.9485	0.1715	0.000000	0.000000
2459498	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	1.990032	1.882456	1.450954	1.507062	0.984234	1.107148	-1.275820	-1.190131	0.9078	0.9183	0.3951	0.000000	0.000000
2459497	digital_ok	0.00%	0.00%	0.00%	0.00%	97.37%	0.00%	2.831304	2.679676	1.664295	1.634623	1.217944	1.376571	-1.110009	-0.954014	0.9227	0.9441	0.3140	0.000000	0.000000
2459496	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	2.066696	2.225081	1.196651	1.419311	0.376282	0.902606	-0.853040	-0.980326	0.8872	0.9042	0.3714	0.000000	0.000000
2459495	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	1.594562	1.740925	1.032703	1.333967	0.285495	0.941511	-0.811902	-0.995912	0.8824	0.8992	0.4103	0.000000	0.000000
2459494	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.657295	3.676239	1.759259	2.134379	1.259155	2.554772	4.256699	6.217862	0.8777	0.8923	0.4229	0.000000	0.000000
2459492	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.545779	0.776931	1.290691	1.222913	0.603283	0.126756	-0.437499	-1.297352	0.7610	0.8064	0.3182	0.000000	0.000000
2459491	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.373326	0.560205	0.639841	0.528497	-0.178651	-0.978450	-0.373798	-1.067278	0.7938	0.8189	0.2635	0.000000	0.000000
2459490	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.652403	1.551572	-0.213858	0.225455	0.273100	0.472972	1.802992	-0.036256	0.7566	0.8027	0.3482	0.000000	0.000000
2459489	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.261115	1.383421	0.508529	0.565501	1.168978	0.746828	-1.010282	-0.520031	0.7733	0.8097	0.3023	0.000000	0.000000
2459488	digital_ok	0.00%	0.00%	0.00%	0.00%	85.71%	14.29%	1.266648	1.270363	1.119386	1.271277	-0.380626	-0.016210	-1.700924	-1.667701	0.7746	0.8125	0.3328	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 103 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 103, Node 8:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 103: 2459625

Antenna 103: 2459624

Antenna 103: 2459623

Antenna 103: 2459622

Antenna 103: 2459621

Antenna 103: 2459620

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Antenna 103: 2459617

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Antenna 103: 2459614

Antenna 103: 2459613

Antenna 103: 2459612

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Antenna 103: 2459609

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Antenna 103: 2459566

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Antenna 103: 2459562

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Antenna 103: 2459556

Antenna 103: 2459552

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Antenna 103: 2459542

Antenna 103: 2459541

Antenna 103: 2459540

Antenna 103: 2459536

Antenna 103: 2459535

Antenna 103: 2459534

Antenna 103: 2459533

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶